The key idea behind object localization in YOLO [1]} is to first estimate the centroid of each object and then to estimate the bounding box (center \(x\) , center \(y\) , width, and height) that encloses an instance. If we can specify a sufficiently rich differentiable function that similarly estimates the complex shape of an instance, going beyond the simple rectangular shape of the bounding box, we can use that function and the parameter regression idea to perform instance segmentation.
